scholarly journals General Review of Rainfall-Runoff Modeling: Model Calibration, Data Assimilation, and Uncertainty Analysis

2008 ◽  
pp. 1-24 ◽  
Author(s):  
Hamid Moradkhani ◽  
Soroosh Sorooshian
Keyword(s):  
Data Assimilation ◽  
Uncertainty Analysis ◽  
Model Calibration ◽  
Calibration Data ◽  
Rainfall Runoff ◽  
General Review ◽  
Runoff Modeling

scholarly journals The HOOPLA toolbox: a HydrOlOgical Prediction LAboratory to explore ensemble rainfall-runoff modeling

2020 ◽  
Author(s):  
Antoine Thiboult ◽  
Gregory Seiller ◽  
Carine Poncelet ◽  
François Anctil
Keyword(s):  
Data Assimilation ◽  
Potential Evapotranspiration ◽  
Hydrological Modelling ◽  
Hydrological Models ◽  
Rainfall Runoff ◽  
Runoff Modeling ◽  
Technical Report ◽  
Flexible Framework ◽  
Hydrological Prediction ◽  
Easy Integration

Abstract. This technical report introduces the HydrOlOgical Prediction LAboratory (HOOPLA) developed at Université Lavalfor ensemble lumped hydrological modelling. HOOPLA includes functionalities to perform calibration, simulation, and forecast for multiple hydrological models and various time steps. It includes a range of hydrometeorological tools such as calibration algorithms, data assimilation techniques, potential evapotranspiration formulas and a snow accounting routine. HOOPLA is a flexible framework coded in MATLAB that allows easy integration of user-defined hydrometeorological tools. This report also illustrates HOOPLA's functionalities using a set of 31 Canadian catchments.

scholarly journals Physically-based distributed hydrological model calibration based on a short period of streamflow data: case studies in two Chinese basins

2016 ◽  
Author(s):  
Wenchao Sun ◽  
Yuanyuan Wang ◽  
Xingqi Cui ◽  
Jingshan Yu ◽  
Depeng Zuo ◽  
...  
Keyword(s):  
Uncertainty Analysis ◽  
Model Calibration ◽  
Calibration Data ◽  
Hydrological Models ◽  
Daily Streamflow ◽  
Short Period ◽  
Analysis Scheme ◽  
Physically Based ◽  
Streamflow Data ◽  
Distributed Hydrological Models

Abstract. Physically-based distributed hydrological models are widely used for hydrological simulations in various environments. However, as with conceptual models, they are limited in data-sparse basin by the lack of streamflow data for calibration. Short periods of observational data (less than 1 year) may be obtained from the fragmentary historical records of past-existed gauging stations or from temporary gauging during field surveys, which might be of values for model calibration. This study explored how the use of limited continuous daily streamflow data might support the application of a physically-based distributed model in data-sparse basins. The influence of the length of observation period on the calibration of the widely applied Soil and Water Assessment Tool model was evaluated in two Chinese basins with differing climatic and geophysical characteristics. The evaluations were conducted by comparing calibrations based on short periods of data with calibrations based on data from a 3-year period, which were treated as benchmark calibrations for the two basins. To ensure the differences in the model simulations solely come from differences in the calibration data, the Generalized Likelihood Uncertainty Analysis scheme was employed for the automatic calibration and uncertainty analysis. In both basins, contrary to the common understanding of the need for observations over a period of several years, data records with lengths of less than 1 year were shown to calibrate the model effectively, i.e. performances similar to the benchmark calibrations were achieved. The model of wet Jinjiang Basin could be effectively calibrated using a shorter data record (1 month), compared with the arid Heihe Basin (6 months). Even though the two basins are very different, the results demonstrated that data from the wet season and wetter years performed better that data from the dry season and drier year. The results of this study demonstrated that short periods of observations could be a promising solution to the problem of calibration of physically-based distributed hydrological models in data-sparse basins and further researches similar to this study are required to gain more general understandings about the optimum number of observations needed for calibration when such model are applied to real data-sparse basins.

Uncertainty analysis of calibrated parameter values of an urban storm water quality model using metropolis monte carlo algorithm

1997 ◽  
Vol 36 (5) ◽  
pp. 141-148 ◽  
Author(s):  
A. Mailhot ◽  
É. Gaume ◽  
J.-P. Villeneuve
Keyword(s):  
Monte Carlo ◽  
Uncertainty Analysis ◽  
Model Calibration ◽  
Monte Carlo Algorithm ◽  
Storm Water ◽  
Model Parameters ◽  
Calibration Data ◽  
Parameter Uncertainties ◽  
Metropolis Monte Carlo ◽  
Parameter Values

The Storm Water Management Model's quality module is calibrated for a section of Québec City's sewer system using data collected during five rain events. It is shown that even for this simple model, calibration can fail: similarly a good fit between recorded data and simulation results can be obtained with quite different sets of model parameters, leading to great uncertainty on calibrated parameter values. In order to further investigate the lack of data and data uncertainty impacts on calibration, we used a new methodology based on the Metropolis Monte Carlo algorithm. This analysis shows that for a large amount of calibration data generated by the model itself, small data uncertainties are necessary to significantly decrease calibrated parameter uncertainties. This also confirms the usefulness of the Metropolis algorithm as a tool for uncertainty analysis in the context of model calibration.

scholarly journals Sensitivity of the performance of a conceptual rainfall–runoff model to the temporal sampling of calibration data

2012 ◽  
Vol 44 (3) ◽  
pp. 484-494 ◽  
Author(s):  
Satish Bastola ◽  
Conor Murphy
Keyword(s):  
Temporal Resolution ◽  
Model Calibration ◽  
Flood Frequency Analysis ◽  
Calibration Data ◽  
Rainfall Runoff ◽  
Time Step ◽  
Scaling Relationship ◽  
Simple Scaling ◽  
Rainfall Runoff Model ◽  
Runoff Model

The effect of the time step of calibration data on the performance of a hydrological model is examined through a numerical experiment where HYMOD, a rainfall–runoff model, is calibrated with data of varying temporal resolution. A simple scaling relationship between the parameters of the model and modelling time step is derived which enables information from daily hydrological records to be used in modelling at time steps much shorter than daily. Model parameters were found to respond differently depending upon the degree of aggregation of calibration data. A loss in performance, especially in terms of the Nash–Sutcliffe measure, is evident when behavioural simulators derived with one modelling time step are used for simulation at another time step. The loss in performance is greater when parameters derived from a longer time step were used for simulating flow with a shorter time step. The application of a simple scaling relationship derived from a multi-time step model calibration significantly decreased the loss in model performance. Such an approach may offer the prospect of conducting higher temporal resolution flood frequency analysis when finer scale data for model calibration are not available or limited.

scholarly journals Physically based distributed hydrological model calibration based on a short period of streamflow data: case studies in four Chinese basins

2017 ◽  
Vol 21 (1) ◽  
pp. 251-265 ◽  
Author(s):  
Wenchao Sun ◽  
Yuanyuan Wang ◽  
Guoqiang Wang ◽  
Xingqi Cui ◽  
Jingshan Yu ◽  
...  
Keyword(s):  
Uncertainty Analysis ◽  
Model Calibration ◽  
Conceptual Models ◽  
Calibration Data ◽  
Hydrological Models ◽  
Short Period ◽  
Analysis Scheme ◽  
Physically Based ◽  
Streamflow Data ◽  
Distributed Hydrological Models

Abstract. Physically based distributed hydrological models are widely used for hydrological simulations in various environments. As with conceptual models, they are limited in data-sparse basins by the lack of streamflow data for calibration. Short periods of observational data (less than 1 year) may be obtained from fragmentary historical records of previously existing gauging stations or from temporary gauging during field surveys, which might be of value for model calibration. However, unlike lumped conceptual models, such an approach has not been explored sufficiently for physically based distributed models. This study explored how the use of limited continuous daily streamflow data might support the application of a physically based distributed model in data-sparse basins. The influence of the length of the observation period on the calibration of the widely applied soil and water assessment tool model was evaluated in four Chinese basins with differing climatic and geophysical characteristics. The evaluations were conducted by comparing calibrations based on short periods of data with calibrations based on data from a 3-year period, which were treated as benchmark calibrations of the four basins, respectively. To ensure the differences in the model simulations solely come from differences in the calibration data, the generalized likelihood uncertainty analysis scheme was employed for the automatic calibration and uncertainty analysis. In the four basins, contrary to the common understanding of the need for observations over a period of several years, data records with lengths of less than 1 year were shown to calibrate the model effectively, i.e., performances similar to the benchmark calibrations were achieved. The models of the wet Jinjiang and Donghe basins could be effectively calibrated using a shorter data record (1 month), compared with the dry Heihe and upstream Yalongjiang basins (6 months). Even though the four basins are very different, when using 1-year or 6-month (covering a whole dry season or rainy season) data, the results show that data from wet seasons and wet years are generally more reliable than data from dry seasons and dry years, especially for the two dry basins. The results demonstrated that this idea could be a promising approach to the problem of calibration of physically based distributed hydrological models in data-sparse basins, and findings from the discussion in this study are valuable for assessing the effectiveness of short-period data for model calibration in real-world applications.

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